HIKONE, SHIGA PREF. – An article in the Sept. 8, 2015, online edition of the Toyo Keizai economic journal featured the types of jobs that increased and decreased in number over the period from 1995 to 2010. Topping the list of jobs that increased was care-giving, which rose by 1.25 million, followed by store work (510,000), medical nursing (410,000), cleaning (250,000), child care (160,000) and cooking (120,000).

A majority of the jobs that increased over the 15-year period — which saw progress in the computerization of society — were linked to the rapid aging of Japan’s population and the rise in the number of households in which both spouses work.

The biggest decline was found in the farming sector, which saw 1.26 million jobs disappear, followed by construction and civil engineering (1.23 million) and accounting (1.13 million). Further down on the list are real estate and insurance brokers (700,000), managers of corporations and organizations (620,000), retail store owners/managers (590,000), corporate executives (560,000), drivers (470,000), and printing and bookbinding (160,000).

The drop in the farming sector was a continuation of an existing trend. The decline in construction jobs was ascribed to the expanded use of prefabricated building materials and cuts in the government’s public works investments. The falling ranks of managers at retail shops, companies and other organizations and the decline in the number of drivers are likely the result of the post-bubble boom economic downturn. The impact of the digital revolution appears to be limited to accounting, real estate/insurance brokers, and printing and bookbinding.

These data suggest the following two hypotheses: First, while the digital revolution undoubtedly replaced certain jobs with the use of computers, it did virtually nothing to create new employment opportunities that more than made up for the lost jobs. In general terms, it is impossible for new technologies to create new jobs that will more than compensate for job losses that result from technological innovations. Second, what made up for job losses attributed to digital revolution was an increase in jobs linked to changes in the social and economic environment that have nothing to do with computerization.

Indeed, the aging of the population, which progressed independently of the digital revolution, led to sharp increases in the number of people engaged in jobs indispensable for an aging population: care-giving and medical nursing. Likewise, the growing ranks of dual income families, which again is unrelated to the digital revolution, increased the number of workers whose jobs were necessitated by the social trend, that is, child care and cooking.

In addition, people who had to take jobs that they may not have wanted — such as care-giving, working in convenience stores and supermarkets, and cleaning — increased. Many of the people who lost their jobs to computerization and sought to find new work through public employment offices were only able to get low-paying jobs in sectors in which they may have had no interest. What’s fortunate was that the digital revolution did not cause a disastrous rise in unemployment because it progressed along with the rapid aging of the population and a rise in women’s participation in the labor market.

Today, the Fourth Industrial Revolution is in progress — driven by artificial intelligence and robotics. Joint research by the Nomura Research Institute and the University of Oxford has predicted that by 2030 one out of every two workers will lose their jobs as a result of it. If that forecast turns out to be correct, it seems difficult to imagine that enough new employment opportunities will be created to accommodate the roughly 33 million workers who will have lost their jobs. A majority of such workers will have no choice but to take low-paying jobs requiring no special skills, or jobs that are shirked by many because they are deemed “dirty, dangerous and demanding.”

If no measures are taken, AI and robots are certain to push humankind to the rock bottom of misery. There is a widely held view that this unemployment problem can be attenuated if the government guarantees a subsistence level of life by giving every unemployed person a basic income to the tune of ¥80,000 a month. But this is equivalent to half of the nation’s households living on welfare.

Everybody wants to be included in society through work. To be excluded from such an opportunity for an extended period can cause unbearable pain. A deep sense of uncertainty or worthlessness will be felt by anybody living in a society where one out of every two persons is excluded from work.

The Nomura-Oxford joint research has calculated the chance of disappearance for each of the 601 types of jobs on the basis of supervised machine learning. The study has concluded that occupations whose chance of disappearing exceeds two-thirds will actually cease to exist. Needless to say, the study has oversimplified the characteristics of each of the occupations. There are many jobs whose probability of going away may run counter to our common knowledge.

Here is one example: The Nomura-Oxford study gives the occupation of certified public accountants an 85.9 percent chance of disappearing, but I find this figure hard to believe. The task of checking the consistency of entries in financial statements may be done by AI, but the work of a CPA does not end with that. A CPA evaluates the legitimacy of corporate operations by auditing financial statements and confirming their reliability. If necessary, they make suggestions for improving operations. To carry out these and other auditing tasks, CPAs need not only explicit knowledge related to accounting and business administration but also tacit knowledge that can only be acquired through long, wide-ranging experience. Although AI can learn explicit knowledge, it cannot master tacit knowledge that can’t be expressed in words, numbers or mathematical formulas. That is why it’s impossible for AI to totally replace CPAs.

Fortunately, the study shows that scholastic jobs by and large will not disappear. The probability of job disappearance is high for scholars in fields where explicit knowledge is deemed important and standard textbooks exist, and for scholars who use mathematics and logic as scholastic tools. Conversely, the likelihood of job disappearance is low in the field of humanities, which does not have much room for explicit knowledge and does not rely on mathematics.

I cannot help feeling that these findings directly reflect the candid prejudices held by the human “supervisors” of machine learning.